from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-06 14:07:04.597474
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sun, 06, Dec, 2020
Time: 14:07:08
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.3283
Nobs: 132.000 HQIC: -44.4952
Log likelihood: 1393.70 FPE: 2.13919e-20
AIC: -45.2939 Det(Omega_mle): 1.10872e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.546707 0.180267 3.033 0.002
L1.Burgenland 0.129453 0.085985 1.506 0.132
L1.Kärnten -0.307270 0.072783 -4.222 0.000
L1.Niederösterreich 0.087225 0.205319 0.425 0.671
L1.Oberösterreich 0.295876 0.171290 1.727 0.084
L1.Salzburg 0.152570 0.086983 1.754 0.079
L1.Steiermark 0.071085 0.123282 0.577 0.564
L1.Tirol 0.173558 0.082068 2.115 0.034
L1.Vorarlberg 0.019865 0.079319 0.250 0.802
L1.Wien -0.143190 0.163349 -0.877 0.381
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.548468 0.231962 2.364 0.018
L1.Burgenland -0.004824 0.110643 -0.044 0.965
L1.Kärnten 0.341139 0.093655 3.643 0.000
L1.Niederösterreich 0.127331 0.264198 0.482 0.630
L1.Oberösterreich -0.199198 0.220410 -0.904 0.366
L1.Salzburg 0.194213 0.111927 1.735 0.083
L1.Steiermark 0.223792 0.158636 1.411 0.158
L1.Tirol 0.143165 0.105602 1.356 0.175
L1.Vorarlberg 0.213756 0.102066 2.094 0.036
L1.Wien -0.567834 0.210192 -2.702 0.007
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.330067 0.078348 4.213 0.000
L1.Burgenland 0.100701 0.037371 2.695 0.007
L1.Kärnten -0.025036 0.031633 -0.791 0.429
L1.Niederösterreich 0.111052 0.089236 1.244 0.213
L1.Oberösterreich 0.283827 0.074446 3.813 0.000
L1.Salzburg -0.013528 0.037805 -0.358 0.720
L1.Steiermark -0.054652 0.053581 -1.020 0.308
L1.Tirol 0.099392 0.035668 2.787 0.005
L1.Vorarlberg 0.141627 0.034474 4.108 0.000
L1.Wien 0.035586 0.070995 0.501 0.616
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.189787 0.092200 2.058 0.040
L1.Burgenland 0.000818 0.043978 0.019 0.985
L1.Kärnten 0.030730 0.037226 0.826 0.409
L1.Niederösterreich 0.045680 0.105013 0.435 0.664
L1.Oberösterreich 0.373103 0.087608 4.259 0.000
L1.Salzburg 0.087691 0.044489 1.971 0.049
L1.Steiermark 0.203509 0.063054 3.228 0.001
L1.Tirol 0.036064 0.041975 0.859 0.390
L1.Vorarlberg 0.114180 0.040569 2.814 0.005
L1.Wien -0.083358 0.083547 -0.998 0.318
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.675238 0.197520 3.419 0.001
L1.Burgenland 0.060137 0.094215 0.638 0.523
L1.Kärnten -0.009688 0.079749 -0.121 0.903
L1.Niederösterreich -0.085498 0.224970 -0.380 0.704
L1.Oberösterreich 0.108429 0.187684 0.578 0.563
L1.Salzburg 0.039567 0.095308 0.415 0.678
L1.Steiermark 0.113308 0.135082 0.839 0.402
L1.Tirol 0.233766 0.089923 2.600 0.009
L1.Vorarlberg 0.042557 0.086911 0.490 0.624
L1.Wien -0.156334 0.178983 -0.873 0.382
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.243173 0.135434 1.796 0.073
L1.Burgenland -0.054443 0.064600 -0.843 0.399
L1.Kärnten -0.019927 0.054682 -0.364 0.716
L1.Niederösterreich 0.165259 0.154255 1.071 0.284
L1.Oberösterreich 0.398971 0.128689 3.100 0.002
L1.Salzburg -0.040946 0.065350 -0.627 0.531
L1.Steiermark -0.057850 0.092621 -0.625 0.532
L1.Tirol 0.203230 0.061657 3.296 0.001
L1.Vorarlberg 0.045491 0.059592 0.763 0.445
L1.Wien 0.130830 0.122723 1.066 0.286
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.251029 0.172091 1.459 0.145
L1.Burgenland 0.061002 0.082086 0.743 0.457
L1.Kärnten -0.081739 0.069482 -1.176 0.239
L1.Niederösterreich -0.108377 0.196007 -0.553 0.580
L1.Oberösterreich -0.084674 0.163521 -0.518 0.605
L1.Salzburg 0.010488 0.083038 0.126 0.899
L1.Steiermark 0.370777 0.117691 3.150 0.002
L1.Tirol 0.540983 0.078346 6.905 0.000
L1.Vorarlberg 0.234787 0.075722 3.101 0.002
L1.Wien -0.187936 0.155940 -1.205 0.228
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.085268 0.200470 0.425 0.671
L1.Burgenland 0.038618 0.095622 0.404 0.686
L1.Kärnten -0.074656 0.080940 -0.922 0.356
L1.Niederösterreich 0.165099 0.228329 0.723 0.470
L1.Oberösterreich 0.028982 0.190487 0.152 0.879
L1.Salzburg 0.226143 0.096731 2.338 0.019
L1.Steiermark 0.195538 0.137099 1.426 0.154
L1.Tirol 0.052318 0.091266 0.573 0.566
L1.Vorarlberg 0.018993 0.088209 0.215 0.830
L1.Wien 0.273761 0.181656 1.507 0.132
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.615943 0.110401 5.579 0.000
L1.Burgenland -0.021097 0.052660 -0.401 0.689
L1.Kärnten -0.002968 0.044575 -0.067 0.947
L1.Niederösterreich -0.052773 0.125743 -0.420 0.675
L1.Oberösterreich 0.293145 0.104903 2.794 0.005
L1.Salzburg 0.006589 0.053271 0.124 0.902
L1.Steiermark 0.004357 0.075502 0.058 0.954
L1.Tirol 0.079316 0.050261 1.578 0.115
L1.Vorarlberg 0.187748 0.048578 3.865 0.000
L1.Wien -0.101614 0.100040 -1.016 0.310
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.099357 -0.054493 0.178658 0.236586 0.010051 0.066265 -0.127001 0.120037
Kärnten 0.099357 1.000000 -0.055640 0.179944 0.099410 -0.168283 0.192482 0.012834 0.270332
Niederösterreich -0.054493 -0.055640 1.000000 0.239255 0.055041 0.163016 0.077813 0.048042 0.353632
Oberösterreich 0.178658 0.179944 0.239255 1.000000 0.250851 0.265808 0.073569 0.069008 0.046280
Salzburg 0.236586 0.099410 0.055041 0.250851 1.000000 0.127982 0.043490 0.083478 -0.055689
Steiermark 0.010051 -0.168283 0.163016 0.265808 0.127982 1.000000 0.083202 0.079848 -0.185950
Tirol 0.066265 0.192482 0.077813 0.073569 0.043490 0.083202 1.000000 0.135364 0.094418
Vorarlberg -0.127001 0.012834 0.048042 0.069008 0.083478 0.079848 0.135364 1.000000 0.064850
Wien 0.120037 0.270332 0.353632 0.046280 -0.055689 -0.185950 0.094418 0.064850 1.000000